滑坡编录模式常为点和多边形面，滑坡点的定位及多边形的采样范围会给滑坡易发性评价结果产生影响.为研究不同点和多边形滑坡样本采样方式下的易发性结果差异，以四川省宁南县为例，采用滑坡多边形和陡坎缓冲区来比较不同多边形表达模式对易发性评价的影响，用滑坡陡坎点和滑坡质心点来比较不同点表达模式对易发性评价的影响，选取3种评价模型支持向量机（SVM）、随机森林（RF）和人工神经网络（ANN）进行滑坡易发性建模，采用ROC曲线、均值、标准差等分析建模的差异.结果如下：（1）在滑坡样本为多边形表达模式下，陡坎缓冲区的评价效果优于滑坡多边形.在滑坡样本为点表达模式下，滑坡质心点的评价效果优于滑坡陡坎点.（2）RF模型在不同采样方式下易发性评价效果更好，不同采样方式下基于RF模型的易发性结果差异性也较小，相比SVM和ANN模型有更好的泛化能力.（3）离散型因子是导致点表达模式下采样方式易发性结果差异的主要因素.陡坎缓冲区采样方式相比于滑坡多边形保留如岩组等离散型环境因子的空间信息，因此评价效果较好.可见在县级尺度下使用滑坡陡坎区域等精细化地形特征作为滑坡采样方式可以提高易发性评价精度.

Landslide cataloging modes are usually points and polygons. The location of landslide points and the sampling range of polygons will affect the results of landslide susceptibility evaluation. In order to study the differences in the susceptibility results of different points and polygonal landslide sample sampling strategies, taking Ningnan County, Sichuan Province as an example, landslide polygons and landslide steep sill buffer zones were used to compare the susceptibility evaluation of different polygon expression patterns. The influence of landslide sill point and landslide mass center point was used to compare the influence of different point expression patterns on susceptibility evaluation, and three evaluation models were selected, namely, support vector machine (SVM), random forest (RF) and artificial neural network (ANN). Landslide susceptibility modeling was performed, and differences in modeling were analyzed using ROC curve, mean, and standard deviation. The results are as follows: (1) When the landslide samples are in the polygonal expression mode, the evaluation effect of the steep sill buffer zone is better than that of the landslide polygon. When the landslide sample is in a point expression mode, the evaluation effect of the landslide mass center point is better than that of the landslide steep point. (2) The susceptibility evaluation effect of the RF model is better under different sampling strategies, and the susceptibility results based on the RF model under different sampling strategies are also less different, and have better generalization ability than the SVM and ANN models. (3) The discrete factor is the main factor leading to the difference in the susceptibility results of the sampling strategy under the point expression pattern. Compared with the landslide polygon, the sampling strategy of the steep sill buffer preserves the spatial information of discrete environmental factors such as rock formations, so the evaluation effect is better. It can be seen that using refined terrain features such as landslide steep ridge areas as landslide sampling methods at the county scale can improve the accuracy of susceptibility evaluation.